Adaptive Invariant Extended Kalman Filter for Legged Robot State Estimation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912659391119360 |
|---|---|
| author | Kim, Kyung-Hwan Ahn, DongHyun Lee, Dong-hyun Yoon, JuYoung Hyun, Dong Jin |
| author_facet | Kim, Kyung-Hwan Ahn, DongHyun Lee, Dong-hyun Yoon, JuYoung Hyun, Dong Jin |
| contents | State estimation is crucial for legged robots as it directly affects control performance and locomotion stability. In this paper, we propose an Adaptive Invariant Extended Kalman Filter to improve proprioceptive state estimation for legged robots. The proposed method adaptively adjusts the noise level of the contact foot model based on online covariance estimation, leading to improved state estimation under varying contact conditions. It effectively handles small slips that traditional slip rejection fails to address, as overly sensitive slip rejection settings risk causing filter divergence. Our approach employs a contact detection algorithm instead of contact sensors, reducing the reliance on additional hardware. The proposed method is validated through real-world experiments on the quadruped robot LeoQuad, demonstrating enhanced state estimation performance in dynamic locomotion scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Invariant Extended Kalman Filter for Legged Robot State Estimation Kim, Kyung-Hwan Ahn, DongHyun Lee, Dong-hyun Yoon, JuYoung Hyun, Dong Jin Robotics Systems and Control State estimation is crucial for legged robots as it directly affects control performance and locomotion stability. In this paper, we propose an Adaptive Invariant Extended Kalman Filter to improve proprioceptive state estimation for legged robots. The proposed method adaptively adjusts the noise level of the contact foot model based on online covariance estimation, leading to improved state estimation under varying contact conditions. It effectively handles small slips that traditional slip rejection fails to address, as overly sensitive slip rejection settings risk causing filter divergence. Our approach employs a contact detection algorithm instead of contact sensors, reducing the reliance on additional hardware. The proposed method is validated through real-world experiments on the quadruped robot LeoQuad, demonstrating enhanced state estimation performance in dynamic locomotion scenarios. |
| title | Adaptive Invariant Extended Kalman Filter for Legged Robot State Estimation |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2510.16755 |